We use an evaluation protocol similar to [1]}, [2]}, [3]}. For each dataset, a full user-item preference matrix \((\mu _{i,j})_{i,j}\)  is obtained by standard matrix factorization algorithmsUsing the Python library Implicit: https://github.com/benfred/implicit (MIT License). from the incomplete interaction matrix, following the protocol of [2]}. Rankings are inferred from these estimated preferences. The exposure weights \({b}\)  are the standard weights of the discounted cumulative gain (DCG) (also used in e.g., [5]}, [6]}, [7]}): \(\forall k \in [\![K]\!], {b}_k = \frac{1}{\log _2(1+k)}.\)
